11/18/2019

Research Question

This project is trying to investigate if investment in different industry segments would impact on the amount of CO2 emission. Varibales being choosed are CO2 emissions total, CO2 emissions per capita, CO2 emissions per GDP, investment in energy, investment in telecommunication, investment in transportation, investment in water.

Variables Choosen

Outcomes

  • CO2 emissions total
  • CO2 emissions per capita
  • CO2 emissions per GDP

Predictors

  • Investment in energy
  • Investment in telecommunication
  • Investment in transportation
  • Investment in water

Get Data

Data Cleaning

## [1] "countrycode" "countryname" "seriescode"  "seriesname"  "SCALE"      
## [6] "Decimals"    "year"        "value"

total CO2 emission between year 1990 and year 2011 for all countries

ggplot(spreadtest,aes(x=countryname,y=CO2total,color=year))+geom_point()+geom_smooth()

CO2 percapita between year 1990 and year 2011 for all countries

ggplot(spreadtest,aes(x=countryname,y=CO2percapita,color=year))+geom_point()+geom_smooth()

the CO2 emission per GDP between year 1990 and year 2011 for selected countries

plot_ly(selected,x=~as.numeric(as.character(year)),y=~CO2perGDP,type='scatter',mode='lines')

Top emission from country (CO2 emission per GDP)

Comparing Investment in Different Segments with CO2 Emission for Each Selected Country

Use the 13 Countries as a Sample to Test if Investment in Different Segments would Impact the Amount of CO2 Emission

Correlation test

## 
##  Pearson's product-moment correlation
## 
## data:  x and y
## t = 0.52495, df = 109, p-value = 0.6007
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1374618  0.2344165
## sample estimates:
##        cor 
## 0.05021779
## 
##  Pearson's product-moment correlation
## 
## data:  x and y
## t = -0.39648, df = 109, p-value = 0.6925
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.2227658  0.1495018
## sample estimates:
##         cor 
## -0.03794859
## 
##  Pearson's product-moment correlation
## 
## data:  x and y
## t = -0.85622, df = 109, p-value = 0.3938
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.2641061  0.1062751
## sample estimates:
##         cor 
## -0.08173693